FT-NIR and linear discriminant analysis to classify chickpea seeds produced with harvest aid chemicals.
Food Chem
; 342: 128324, 2021 Apr 16.
Article
em En
| MEDLINE
| ID: mdl-33069535
ABSTRACT
Spectroscopy and machine learning (ML) algorithms have provided significant advances to the modern food industry. Instruments focusing on near-infrared spectroscopy allow obtaining information about seed and grain chemical composition, which can be related to changes caused by field pesticides. We investigated the potential of FT-NIR spectroscopy combined with Linear Discriminant Analysis (LDA) to discriminate chickpea seeds produced using different desiccant herbicides at harvest anticipation. Five herbicides applied at three moments of the plant reproductive stage were utilized. The NIR spectra obtained from individual seeds were used to build ML models based on LDA algorithm. The models developed to identify the herbicide and the plant phenological stage at which it was applied reached 94% in the independent validation set. Thus, the LDA models developed using near-infrared spectral data provided to be efficient, quick, non-destructive, and accurate to identify differences between seeds due to pre-harvest herbicides application.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Sementes
/
Espectroscopia de Luz Próxima ao Infravermelho
/
Cicer
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article